Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation

A cloud server spent a lot of time, energy and money to train a Viola-Jones type object detector with high accuracy. Clients can upload their photos to the cloud server to find objects. However, the client does not want the leakage of the content of his/her photos. In the meanwhile, the cloud server is also reluctant to leak any parameters of the trained object detectors. 10 years ago, Avidan & Butman introduced Blind Vision, which is a method for securely evaluating a Viola-Jones type object detector. Blind Vision uses standard cryptographic tools and is painfully slow to compute, taking a couple of hours to scan a single image. The purpose of this work is to explore an efficient method that can speed up the process. We propose the Random Base Image (RBI) Representation. The original image is divided into random base images. Only the base images are submitted randomly to the cloud server. Thus, the content of the image can not be leaked. In the meanwhile, a random vector and the secure Millionaire protocol are leveraged to protect the parameters of the trained object detector. The RBI makes the integral-image enable again for the great acceleration. The experimental results reveal that our method can retain the detection accuracy of that of the plain vision algorithm and is significantly faster than the traditional blind vision, with only a very low probability of the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul 14, 2017, Hong Kong, Hong Kon

A Privacy-Preserving Framework for Large-Scale Content-Based Information Retrieval Using K-Secure Sum Protocol

We propose a privacy protection framework for large-scale content-based information retrieval. It offers two layers of protection. To begin with, robust hash values are utilized as quiries to avoid uncovering unique content or features. Second, the customer can choose to exclude certain bits in a hash values to further expand the ambiguity for the server. Due to the reduced information, it is computationally difficult for the server to know the customer's interest. The server needs to give back the hash values of every single possible to the customer. The customer performs a search within the candidate list to locate the best match. Since just hash values are exchanged between the client and the server, the privacy of both sides is ensured. We present the idea of tunable privacy, where the privacy protection level can be balanced by policy. It is acknowledged through hash-based piecewise inverted indexing. The thought is to gap a highlight vector into pieces and list every piece with a sub hash value. Each sub hash value is connected with an inverted index list. The framework has been broadly tested using a large scale image database. We have assessed both retrieval performance and privacy-preserving performance for a specific content identification application. Two unique developments of robust hash algorithms are utilized. One depends on random projections; the other depends on the discrete wavelet transform. Both algorithm exhibit satisfactory performances in comparison with state-of-the-art retrieval performances. The outcomes demonstrate that the privacy upgrade somewhat enhances the retrieval performance. We consider the majority voting attack for evaluating the query category and identification. The test results demonstrate that this attack is a threat when there are close duplicities, yet the achievement rate diminishes with the quantity of discarded bits and the number of distinct items

A Survey Paper on Secure Privacy Preserving Structure for Content Based Information Retrieval on Large Scale

It is very essential to protect personal confidential data that we share or search through web. Previously there are number of privacy preserving mechanism has been developed. Here we develop a new privacy protection framework for huge- content-based information retrieval. We are offering protection in two layers. Initially, robust hash values are taken as queries to avoid revealing of unique features or content. Then, the client has to select to skip some of the bits in a hash value for increasing the confusion for the server. Since we are reducing information it is not so easy for servers to know about interest of the client. The server needs to give back the hash values of all promising candidates to the client. The client will find the best match by searching in the candidate list. Because we are only sharing hash values between server and client the privacy of client and server will be protected. We begin the idea of tunable privacy, where we can adjust level of privacy protection according to the policy. We can realized it by hash based. It can be realized through piecewise inverted indexing based on hash. We have to divide extracted feature vector into pieces and index each and every piece with a value. Every value is linked with an inverted index list. The framework has been comprehensively tested with very huge image database. We have estimated both privacy-preserving performance and retrieval performance for those content recognition application. Couple of robust hash algorithm is being used. One is based on discrete wavelet transform; the other is based on the random projections. Both of these algorithms demonstrate acceptable recital in association with state-of-the-art retrieval schemes. We believe the bulk voting attack for guesstimate the query recognition and sort. Experiment results confirm that this attack is a peril when there are near-duplicates, but the success rate is depends upon the number of distinct item and omitted bits, success rate decrees when omitted bits are increased

A Novel Approach for Preserving Privacy of Content Based Information Reterival System

Content based information retrieval system (CBIR) are advanced version of retrieval systems where search is based upon specific criteria in order to get relevant items. In networking environment, as search is based on content it is easy for server to know client’s interest, where client has to trust server to get relevant items. Sometimes query contains sensitive information that client does not want to reveal it, but still search should be performed. This is achieved by our proposed structure, where mainly it will deal with multimedia items such as image or audio files. In order to preserve privacy , client selects multimedia file of which hash value is generated, this value is fired towards cloud server. Cloud server contains database of stored hash values of multimedia items and based upon hamming distance and similarity search, encrypted candidate list is prepared and send it to client. Client finds best item by carrying decryption

A review on visual privacy preservation techniques for active and assisted living

This paper reviews the state of the art in visual privacy protection techniques, with particular attention paid to techniques applicable to the field of Active and Assisted Living (AAL). A novel taxonomy with which state-of-the-art visual privacy protection methods can be classified is introduced. Perceptual obfuscation methods, a category in this taxonomy, is highlighted. These are a category of visual privacy preservation techniques, particularly relevant when considering scenarios that come under video-based AAL monitoring. Obfuscation against machine learning models is also explored. A high-level classification scheme of privacy by design, as defined by experts in privacy and data protection law, is connected to the proposed taxonomy of visual privacy preservation techniques. Finally, we note open questions that exist in the field and introduce the reader to some exciting avenues for future research in the area of visual privacy.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is part of the visuAAL project on Privacy-Aware and Acceptable Video-Based Technologies and Services for Active and Assisted Living (https://www.visuaal-itn.eu/). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 861091. The authors would also like to acknowledge the contribution of COST Action CA19121 - GoodBrother, Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living (https://goodbrother.eu/), supported by COST (European Cooperation in Science and Technology) (https://www.cost.eu/)

Visual Privacy Protection Methods: A Survey

Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambient-assisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual’s privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users’ acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people’s privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.This work has been partially supported by the Spanish Ministry of Science and Innovation under project “Sistema de visión para la monitorización de la actividad de la vida diaria en el hogar” (TIN2010-20510-C04-02) and by the European Commission under project “caring4U - A study on people activity in private spaces: towards a multisensor network that meets privacy requirements” (PIEF-GA-2010-274649). José Ramón Padilla López and Alexandros Andre Chaaraoui acknowledge financial support by the Conselleria d'Educació, Formació i Ocupació of the Generalitat Valenciana (fellowship ACIF/2012/064 and ACIF/2011/160 respectively)